Data kecepatan gelombang S (shear) sangat diperlukan untuk karakterisasi reservoar dalam menentukan zona reservoar. Namun data kecepatan gelombang S sangat terbatas dan tersedia pada sumur tertentu saja. Penelitian ini dilakukan untuk memprediksi nilai kecepatan gelombang S dengan menggunakan metode supervised machine learning pada sumur S-1 lapangan migas di cekungan Sumatra Tengah. Simulasi algoritma machine learning dilakukan melalui tahapan sebelum dan setelah tuning pada algoritma library Scikit learn dan algoritma artificial neural network (ANN). Selain itu, parameter dan jumlah data yang digunakan dalam memprediksi nilai kecepatan gelombang akan menentukan nilai error dan akurasi. Hasil analisis menunjukkan bahwa algoritma yang digunakan untuk memperoleh akurasi terbaik pertama dalam memprediksi kecepatan gelombang S, yaitu random forest dengan nilai parameter n_estimator terbaik 10 dan algoritma kedua yang terbaik yaitu k-nearest neighbor dengan nilai parameter n_neighbor terbaik 5.

Tunu Shallow Zone (TSZ) is one of producing zone in Tunu Field. Tunu Field is a giant gas field located in the present-day Mahakam Delta, East Kalimantan, Indonesia. The gas reservoirs are scattered along the Tunu Shallow Zone and correspond with fluvio-deltaic series and main lithologies are shale, sand and coal layes. The development of TSZ heavily relies on seismic to access and identify gas sand reservoirs as drilling targets. Anomaly seismic is correspond with the gas sand reservoirs, however with the conventional use of seismic that is difficult for differentiating the gas sands from the coal layers. We established Tunu reliable technology which is comprised four different analyses on stacks, CDP Gathers, AVA/AVO, and litho-seismic cube. We are hit high success rate in identifying gas but requires a lot of time to assess the prospect. But the challenge is to access more than 20, 000 shallow geobodies in time manner, faster and more efficient to fulfill our drilling sequences target and speed-up the development phase. Therefore, we are developing seismic driven supervised machine learning to fit learn geological Tunu characteristic to be gas reservoirs. Several machine learning algorithm has been tested and selected based on several criteria such as AVA/AVO, and amplitude of seismic. The algorithm used to learn behavior of seismic correspond with gas reservoir from data training then applied it to validation and blind dataset for evaluating final models. The final machine learning output is gas probability cube with precision of 70-80% precision from well drilled result in term of gas occurence. Furthermore, unsupervised machine learning has been used to extract potential prospecting targets as geobody targets. Initial test showed encouraging result to extract geobody targets in the shorter time compare with the conventional geomodeling. The final goals are optimizing our current workflow for screening shallow gas potentials, accelerate screening in the future well targets with more efficient, effective way and independent of subjectivity, allowing 2G (geologist and geophysicists) explore deeper and confident way when targeting next future shallow gas target. Usage of seismic driven machine learning for targeting shallow gas reservoir is one big step in the current oil and gas industry and in the same time opening more opportunity to maximize powerful machine learning in 4.0 industry era which is need accuracy, more precise, robust, faster and efficient.

Reservoir sub-vulkanik merupakan salah satu hal menarik dalam dunia eksplorasi cadangan migas di masa mendatang. Tantangan dalam eksplorasi pada reservoir sub vulkanik adalah keterbatasan metoda seismik dalam melakukan pencitraan bawah permukaan pada reservoir sub-vulkanik, hal itu dikarenakan keberadaan lapisan vulkanik yang terletak diatas reservoir sub-vulkanik memiliki kecepatan yang kompleks dan dominasi komponen frekuensi rendah sehingga metode konvensional seperti Dix Conversion tidak cukup akurat dalam memodelkan profil Vp di daerah sub-vulkanik. Oleh karena itu dibutuhkan studi komparasi antara metoda gravitasi dan magnetotellurik dimana pada pengolahan inversi secara terpisah metode MT 1.5D menunjukan bahwa MT sensitif terhadap kehadiran fasies vulkanik tetapi tidak cukup sensitif terhadap keberadaan basement. Sedangkan metode gravitasi sensitif terhadap keberadaan basement tetapi tidak cukup sensitif dalam menggambarkan fasies vulkanik.